AI Model Privacy Control Plan
Privacy Controls and Measures for AI Model Lifecycle Management
Introduction
This document outlines the privacy controls and measures applied to the AI model throughout its lifecycle to ensure compliance with data protection regulations and customer requirements. The plan follows best practices in privacy governance, data security, and ethical AI principles.
📝Current Status
At zime.ai we are still at an early stage. We have not developed or fine-tuned our own model yet. But we have our plans to build same hence we have a document for reference.
Privacy by Design & Data Governance
  • Implement Privacy by Design principles at every stage of the AI model lifecycle.
  • Maintain a Data Classification Policy to differentiate between personal, sensitive, and non-sensitive data.
  • Ensure AI models only process the minimum necessary data to function effectively.
Data Collection & Processing Controls
  • Obtain explicit user consent where required before collecting personal data.
  • Implement automated data minimization techniques to ensure unnecessary personal data is not processed.
AI Model Evaluation
  • Use synthetic or de-identified data whenever possible to reduce privacy risks.
  • Perform bias and fairness audits to prevent discriminatory or unethical AI behavior.
Access Controls
  • Restrict access to AI models and datasets using Role-Based Access Control (RBAC).
  • Encrypt data in transit and at rest to ensure confidentiality.
  • Implement logging and monitoring to track AI model inputs and outputs for potential misuse.
AI Model Output Privacy Safeguards[in progress]
  • Prevent AI models from generating personally identifiable information (PII) in responses.
  • Implement automated redaction mechanisms to remove sensitive data from outputs.
  • Regularly review AI-generated content to ensure compliance with privacy regulations.
Data Retention & Deletion Policy[in progress]
  • Define clear retention periods for training and inference data based on compliance requirements.
  • Allow users to request data deletion in compliance with GDPR, CCPA, and other relevant regulations.
  • Establish mechanisms to periodically purge or de-identify stored AI-related data.
Incident Response & Compliance
  • Develop a privacy breach response plan to address potential AI-related data incidents.
  • Conduct regular privacy audits to ensure continued compliance with Zime's privacy policies.
  • Maintain documentation of Privacy Impact Assessments (PIAs) and compliance reports for regulatory review.
Ongoing Monitoring & Improvements
  • Continuously update privacy safeguards based on evolving regulations and AI security threats.
  • Implement an AI Ethics Committee or governance framework to oversee ethical AI development.
  • Regularly engage with stakeholders to assess privacy risks and enhance AI model transparency.
Conclusion
This plan ensures that appropriate privacy controls are in place throughout the AI model lifecycle, aligning with Zime's data protection requirements and industry best practices. The outlined measures will be regularly reviewed and updated to maintain compliance and enhance privacy protection.
🔒Privacy First Approach
Our commitment to privacy extends beyond compliance - it's fundamental to building trust with our enterprise customers and protecting their sensitive sales data.